Currently, the research efforts on machining indices such as tool wear, surface roughness, power consumption etc. is well reported in literature, but energy analysis based on material removal methods and machine learning has received comparatively little attention. Therefore, the present work deals with the research efforts on simultaneous reduction of specific cutting energy in sustainable machining of Inconel 601 alloy with different machine learning models. The studies were conducted using dry, minimum quantity lubrication (MQL), nano-MQL, cryogenic, and hybrid cooling methods (cryo-nano-MQL). The specific cutting energy (SCE) values were calculated based on the data obtained from power consumption and material removal rate. Subsequently, the SCE data is employed to construct the crucial maps, which are then utilized in several sophisticated machine learning models, including Multiple Linear Regression, Lasso Regression, Bayesian Ridge Regression, and Voting Regressor, to facilitate the predictive modeling of outcomes. The findings of the study indicate that the Bayesian model exhibits a comparatively reduced error rate and a closely aligned R2 value when compared to other prediction models. Moreover, as a novelty, nanoparticles addition into hybrid cooling methods (cryo + nano + MQL) also showed better performance as well as 0.3 % less specific cutting energy than only cryo method which is previously used in former studies.
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